PatternLab for Proteomics is an integrated computational environment for analyzing shotgun proteomic data. PatternLab contains modules for formatting sequence databases, performing peptide spectrum matching, statistically filtering and organizing shotgun proteomic data, extracting quantitative information from label-free and chemically labeled data, performing statistics for differential proteomics, displaying results in a variety of graphical formats, performing similarity-driven studies with de novo sequencing data, analyzing time-course experiments, and helping with the understanding of the biological significance of data in the light of the Gene Ontology. Here we present PatternLab for Proteomics 4.0, which closely knits together all these modules in a self-contained environment, covering the principal aspects of proteomic data analysis as freely available and easily installable software.
PatternLab is user-friendly, simple and provides a graphical user interface.
When using PatternLab please cite:
Paulo C Carvalho, Diogo B Lima, Felipe V Leprevost, Marlon D M Santos, Juliana S G Fischer, Priscila F Aquino, James J Moresco, John R Yates III, Valmir C Barbosa, “Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0”,
Nature Protocols (11), 102-117 (2016)
Publications in peer reviewed journals:
- "PatternLab for proteomics: a tool for differential shotgun proteomics.", BMC bioinformatics, v. 9, p. 316, 2008.
- "Analyzing shotgun proteomic data with PatternLab for proteomics.", Current Protocols in Bioinformatics, v. Chapter 13, p. Unit 13.13, 2010.
- "PatternLab: from mass spectra to label-free differential shotgun proteomics.", Current Protocols in Bioinformatics , v. Chapter 13, p. Unit 13.19, 2012.